def _prepare_val_data( self, data: Tuple[Tuple[int, str], Tuple[str, BinaryIO]] ) -> Tuple[Tuple[Label, str], Tuple[str, BinaryIO]]: label_data, image_data = data _, wnid = label_data label = Label.from_category(self._wnid_to_category[wnid], categories=self._categories) return (label, wnid), image_data
def _prepare_sample(self, data: Tuple[str, Any]) -> Dict[str, Any]: path, buffer = data category = pathlib.Path(path).parent.name return dict( label=Label.from_category(category, categories=self._categories), path=path, image=EncodedImage.from_file(buffer), )
def _prepare_sample( self, data: Tuple[str, Tuple[str, BinaryIO]]) -> Dict[str, Any]: id, (path, buffer) = data return dict( label=Label.from_category(id.split("/", 1)[0], categories=self._categories), path=path, image=EncodedImage.from_file(buffer), )
def _prepare_train_data( self, data: Tuple[str, BinaryIO] ) -> Tuple[Tuple[Label, str], Tuple[str, BinaryIO]]: path = pathlib.Path(data[0]) wnid = cast(Match[str], self._TRAIN_IMAGE_NAME_PATTERN.match(path.name))["wnid"] label = Label.from_category(self.info.extra.wnid_to_category[wnid], categories=self.categories) return (label, wnid), data
def _prepare_sample( data: Tuple[str, BinaryIO], *, root: pathlib.Path, categories: List[str], ) -> Dict[str, Any]: path, buffer = data category = pathlib.Path(path).relative_to(root).parts[0] return dict( path=path, data=EncodedData.from_file(buffer), label=Label.from_category(category, categories=categories), )
def _prepare_sample( self, data: Tuple[Tuple[str, List[str]], Tuple[str, BinaryIO]] ) -> Dict[str, Any]: (_, joint_categories_data), image_data = data _, *joint_categories = joint_categories_data path, buffer = image_data category = pathlib.Path(path).parent.name return dict( joint_categories={ category for category in joint_categories if category }, label=Label.from_category(category, categories=self.categories), path=path, image=EncodedImage.from_file(buffer), )
def _prepare_sample( self, data: Tuple[Tuple[str, str], Tuple[Tuple[str, BinaryIO], Tuple[str, BinaryIO]]] ) -> Dict[str, Any]: key, (image_data, ann_data) = data category, _ = key image_path, image_buffer = image_data ann_path, ann_buffer = ann_data image = EncodedImage.from_file(image_buffer) ann = read_mat(ann_buffer) return dict( label=Label.from_category(category, categories=self._categories), image_path=image_path, image=image, ann_path=ann_path, bounding_box=BoundingBox(ann["box_coord"].astype( np.int64).squeeze()[[2, 0, 3, 1]], format="xyxy", image_size=image.image_size), contour=_Feature(ann["obj_contour"].T), )